Overparameterized deep neural networks have enjoyed a tremendous amount of success in a wide range of machine learning applications [1]}, [2]}, [3]}, [4]}.
However, as these highly expressive models have the capacity to have multiple solutions that interpolate training data, and not all these solutions perform well on test data, it is important to characterize which of these interpolating solutions the optimization algorithms converge to.
Such characterization is important as it helps understand the generalization performance of these models, which is one of the most fundamental questions in machine learning.
